PICO
PICO: Wed, 30 Apr | PICO spot 4
Evapotranspiration (ET) is a key component of the global water cycle, influenced by both natural processes and human activities. However, existing ET products, based on hydrological models, satellite observations, and reanalysis data, often exhibit significant disagreements particularly in human-impacted regions. These discrepancies emerge as the major source of uncertainties in closing the water budget at global scale making it a complex and integrated challenge. To address this, researchers have adopted a water-budget based approach for estimating ET using multiple precipitation, runoff and terrestrial water storage change products, thereby ensuring water budget closure. However, this approach results in multiple estimates of ET with large uncertainties, where weighted average approach fails to reduce these uncertainties. To reduce these uncertainties, a novel Kalman filter-based framework is implemented in this study. It combines multiple water budget-based ET estimates to produce a robust, data-driven ET product (KF-ET) which significantly reduces uncertainty (less than 2 mm/month) while achieving the closest approximation of water budget closure. The performance of KF-ET is evaluated at global and basin scale, with comparisons to ERA5, Fluxcom, GLEAM, and WGHM products. Results demonstrate that KF-ET improves on existing products in terms of capturing the spatio-temporal variability in ET with lower uncertainties. KF-ET aids in understanding of inter-annual and seasonal ET variability, especially in regions with complex hydrological dynamics, such as the Ganges and Amazon River Basin. Furthermore, sensitivity of KF-ET to human-driven changes, including irrigation effects, is highlighted through case study in the Ganges where it accounts for flood irrigation during the early stages of crop growth. KF-ET is also consistent with energy-limited nature of ET in Amazon River basin because of abundant precipitation and deep-root water access for trees. This Kalman Filter approach provides a promising framework for synthesizing high-quality, data-driven global ET estimates that incorporate both natural and anthropogenic influences, offering significant steps towards closing the water budget globally. KF-ET can be accessed at: https://doi.org/10.6084/m9.figshare.23800692 (Goswami et al., 2024)
Reference:
Goswami, S., Rajendra Ternikar, C., Kandala, R., Pillai, N. S., Kumar Yadav, V., Abhishek, Joseph, J., Ghosh, S., & Dutt Vishwakarma, B. (2024). Water budget-based evapotranspiration product captures natural and human-caused variability. Environmental Research Letters, 19(9), 094034. https://doi.org/10.1088/1748-9326/ad63bd
Goswami, S., Ternikar, C.R., Kandala, R., Pillai, N.S., et al. (2023) Evapotransiration using Kalman filter on water budget. [Online]. Available from: https://doi.org/10.6084/m9.figshare.23800692.v3 [Accessed: 2 December 2024].
How to cite: Goswami, S., Ternikar, C., Kandala, R., Pillai, N., Yadav, V. K., Abhishek, A., Joseph, J., Ghosh, S., and Vishwakarma, B. D.: A Kalman Filter approach for reducing uncertainty in Global Evapotranspiration: Advancing global water budget closure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1051, https://doi.org/10.5194/egusphere-egu25-1051, 2025.
Evapotranspiration (ET) plays a crucial role in estimating groundwater recharge, which is critical to ensure water resource management, particularly in countries prone to difficult access to water such as Madagascar. This study estimates ten ET products derived from remote sensing (RS), land surface modeling (LSMs), and reanalysis methods. To evaluate the performance of these datasets, we use a water balance approach, comparing precipitation minus runoff (P−Q) at the catchment scale. This comparison covers nine catchments in Madagascar, focusing on humid and semi-humid regions over monthly to annual timescales from 2000 to 2014. We also take advantage of the GRACE/-FO satellite missions (2002–2023) to estimate large-scale variability in water storage. Analyses were performed at different spatial scales (basin level, bioclimatic zone level, and across the entire island) and various timescales (monthly, annual, and interannual). Results highlight significant differences in ET product performances. ERA5 (the fifth-generation ECMWF atmospheric reanalysis) and GLEAM (Global Land Evaporation Amsterdam Model) show the best performance overall. MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) and GLDAS-CLSM (Global Land Data Assimilation System with the Community Land Surface Model) exhibit significant errors and biases. Understanding these differences requires addressing the uncertainties in the input data and the physical methods employed by each ET product. These results allow us to better understand the impact of extreme weather events (e.g. droughts and cyclones) over water and vegetation dynamics spatialized across Madagascar.
How to cite: Alimohammad Nejad, R., D. Carrière, S., Ollivier, C., Oudin, L., Olioso, A., and Chanard, K.: Assessment of ten evapotranspiration estimates using a water balance approach – application to Madagascar., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17813, https://doi.org/10.5194/egusphere-egu25-17813, 2025.
High-resolution climate data significantly enhance the accuracy and understanding of water budgets, particularly in mountainous regions. Evapotranspiration (ET), the largest terrestrial water flux, is a critical parameter for surface water modelling and monitoring climate change impacts on water resources and agriculture. Its influence is especially pronounced in Europe and the Mediterranean, recognized as climate change hotspots.
This study presents a high-resolution (1 km daily) Potential Evapotranspiration (PET) dataset, derived from a combination of ground-based and remote sensing data and adjusted with daily crop growth coefficients. Covering Europe and the Mediterranean region from 2004 to 2022, the dataset is validated through regional, basin, and local scales. Validation is performed using triple collocation metrics combining the PET-1km product, GLEAM (28 km) and hPET (11 km), as well as with daily measurements from 38 eddy covariance (EC) flux tower stations within the study domain. At the basin scale, the Adige River basin in the Italian Alps is modeled using the Adige Hydrological Digital Twin, incorporating PET-1 km as an input component.
Regional-scale validation highlights the superior performance of the PET-1 km dataset, achieving better results in 86.7% of the study area compared to GLEAM and hPET. Site-scale validation against EC measurements indicates a high correlation coefficient (0.82) and a low RMSE (1.08 mm/day). Basin-scale validation in the Adige basin reveals improved modelling of water cycle components compared to previous findings on the same study area.
This high-resolution PET dataset offers valuable insights for climate and hydrological studies, advancing water resource management and climate adaptation strategies in this crucial region.
How to cite: Bouabdelli, S., Massari, C., Morlot, M., Castelli, M., and Formetta, G.: High-Resolution (1km Daily) Potential Evapotranspiration Dataset Over Europe and the Mediterranean Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18052, https://doi.org/10.5194/egusphere-egu25-18052, 2025.
The combined effect of evaporation and transpiration plays a key role in the water balance for hydrological modelling at catchment scale.
However, the direct measurement of these processes is very challenging, and a formula is typically used to estimate the potential evapo-transpiration (PET), i.e. the maximum rate of water leaving the catchment to the atmosphere in ideal conditions.
Many rainfall-runoff models take the PET as input, and improving the quality of the PET data can directly enhance the performance of the model.
Moreover, PET is a crucial factor to characterize the hydrological behavior and to find similarities between basins.
Many PET formulas have been proposed and several of them are commonly used with excellent results, but the choice is up to the single researcher, that each time must decide based on the input data available and their personal preferences.
Here we analyze the differences between formulas in the estimated values, obtained for a large set of Caravan catchments (Kratzert et al., 2023), in order to give insights on which PET formulas could be more suited for the use in rainfall-runoff modelling.
We selected a group of PET formulas among the most used in literature, with diverse types of methods and required inputs.
The data to feed the formulas were taken from global datasets, derived from reanalysis products: the availability in such sets of temperature, radiation, pressure, humidity and wind allows us to include in the study the FAO Penman-Monteith formula, that we used as benchmark. Additionally, we selected some temperature and/or radiation-based formulas, which represent important tools for data-scarce applications and large-scale hydrology.
For every catchment we calculated the daily time series of PET for each formula, then we analyzed and compared the aggregated yearly and seasonal mean values.
We illustrated the main differences and distribution variations between catchments at local and global scale, highlighting climatological patterns and how the choice of the PET formula affects the catchment aridity classification in the Budyko curve.
References:
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., and Matias, Y.: Caravan – A global community dataset for large-sample hydrology. Sci Data 10, 61 (2023). https://doi.org/10.1038/s41597-023-01975-w
How to cite: Selleri, G., Neri, M., and Toth, E.: Which PET formulas for my rainfall-runoff modelling?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11531, https://doi.org/10.5194/egusphere-egu25-11531, 2025.
Evapotranspiration (ET) returns more than half of the precipitation into the atmosphere, playing an important role in the water cycle. Past research indicates that global ET shows an increasing trend under climate change. However, the dominant drivers of ET differ between climate zones, plants also react differently under different environmental conditions. Research on the sensitivity of ET to combinations of multiple environmental factors remains limited. We focus on the Gaoping River Basin located in southern Taiwan, using the 2-kilometer gridded ET and meteorological data, including Temperature (T), Solar radiation (SR) and Vapor Pressure Deficit (VPD) from 1980 to 2021 produced by Taiwan Climate Change Projection Information and Adaption knowledge Platform (TCCIP), combined with an Artificial Neural Network (ANN) to develop the model. We divide the data into bins based on the percentiles of environmental factors to represent various environmental conditions, the sensitivity of ET to changes in environmental factors was calculated in each bin. The objectives of this study include (1) Identifying the dominant drivers of ET within Gaoping River Basin; (2) Examining whether the sensitivity of ET to environmental factors exhibits seasonal or interannual variations; and (3) Assessing the potential impacts of changes in environmental factors on ET under climate change. Preliminary results show that ET is most sensitive to T within Gaoping River, and the sensitivity of ET to SR varies between seasons.
How to cite: Chen, S.-E., Yeh, H.-H., Chiu, C.-C., and Lee, T.-Y.: The potential impacts of environmental factors change on evapotranspiration in the Gaoping River Basin, Taiwan: A sensitivity analysis using Artificial Neural Networks (ANN), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4806, https://doi.org/10.5194/egusphere-egu25-4806, 2025.
The depletion of terrestrial water storage (TWS) in the Himalayas has profound implications for water security, affecting billions of people downstream. However, the mechanisms behind this depletion remain highly debated. In this study, we update the water storage budget for the Himalayas, revealing that nearly all TWS depletion (−11.12 ± 1.26 Gt yr⁻¹) during 2003–2016 were from glacier mass loss (−11.69 ± 0.32 Gt yr⁻¹). The glacier evolution modeling shows that 75 ± 13% of this glacier mass loss would have occurred even if climate conditions had stabilized during this period. This lagged response to the imbalance with previous climatic conditions is modulated by the long glacier response time (the time required to reach a new equilibrium after a climatic perturbation), with a mean value of 45.6 years across the Himalayas. Our findings indicate that the observed TWS depletion is primarily a lagged response to past climate change, rather than a direct consequence of concurrent climatic conditions as previously claimed. This study reconciles existing debates by emphasizing the critical role of multi-decadal glacier response time in TWS dynamics over glaciated regions, implying that current climatic variations will continue to influence TWS changes in the coming decades, even if no further climate change.
How to cite: Zou, Y., Yang, Y., and Lu, X.: Glacier lagged response dominated the Himalayan water storage depletion, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8447, https://doi.org/10.5194/egusphere-egu25-8447, 2025.
Groundwater is a vital freshwater resource that sustains agriculture, domestic water supply and industrial growth, particularly in the arid and semi-arid regions around the globe experiencing low rainfall. Excessive groundwater abstraction for sustained crop production and water demand for the growing population has led to overexploitation of the freshwater resource globally. Multiple studies have reported significant groundwater depletion in major aquifers around the globe. It is imperative to monitor the availability groundwater at diverse spatial and temporal scales accurately to reduce the uncertainty in groundwater supply and formulate policies for sustainable groundwater resource management.
A major leap in the estimation of groundwater resource emerged after the launch of Gravity Recovery and Climate Experiment Mission (GRACE) in 2002. Integration of Terrestrial Water Storage (TWS) estimates from GRACE/GRACE FO and water storage components (surface runoff, evapotranspiration and soil moisture) from global hydrological models are employed to monitor groundwater storage variability in major aquifers globally. However, uncertainties have been reported in the estimation of groundwater depletion, majorly due to the course resolution of GRACE TWS and model uncertainties at regional scales. Hence, it is important to identify the uncertainties in ancillary datasets employed to monitor groundwater variability from space.
In the present study, we evaluated long term trends in terrestrial and groundwater storage anomaly (2002-2023) over 4 major river basins of western India using three GRACE/GRACE FO mascon solutions – JPL, CSR and GSFC and computed groundwater storage anomalies using a combination of TWSA and Soil moisture estimates from multiple hydrological models – GLDAS Catchment Land Surface Model (CLSM), WaterGAP and ESA_CCI Combined data products. We further evaluated the performance of a global downscaled water storage anomaly product generated from self-supervised data assimilation. It was observed that the groundwater estimates from multi-temporal datasets showed uncertainties in long term trends when validated against in-situ groundwater observations. Further, heterogeneity in inter-basin groundwater variability was observed, indicating a need for estimation of water storage components at regional scale incorporating human intervention for accurate groundwater measurement. Our results highlight the importance of groundwater change estimation from earth observation and modelled hydrological components to insights into changing dynamics of groundwater in semi-arid river basins.
How to cite: Choubey, S., Chander, S., and Kumari, R.: Estimation of Groundwater Storage Variability over Major River Basins of Western India using Multi-Temporal Satellite Datasets and in-situ observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15859, https://doi.org/10.5194/egusphere-egu25-15859, 2025.
Integrating multi-source remote sensing data with hydrological models presents significant challenges, primarily due to mismatches in spatial resolution between satellite observations and models, and spectral inconsistencies between model outputs and observations. These discrepancies stem from satellite mission sampling, the conversion of measured signals to the variables of interest, and processing steps like filtering to reduce noise. For instance, Terrestrial Water Storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) represent a vertical summation of all water stored on land, with a footprint of several hundred kilometers. Another example is Surface Soil Moisture (SSM) data from passive and active remote sensing missions, such as the ESA Climate Change Initiative (CCI), which reflects the moisture of the top few centimeters of soil at a spatial resolution of 25 km.
While large-scale hydrological models now target kilometer-level spatial resolution, their ability to represent climate-driven and anthropogenic changes remains limited. In this study, we propose a hierarchical Bayesian approach to merge GRACE/GRACE-FO TWS changes and ESA CCI’s SSM with the water storage outputs of a high-resolution hydrological model, while accounting for uncertainties in both observational data and model simulations. Our methodology aims to downscale GRACE/GRACE-FO observations and achieve vertical separation of GRACE/GRACE-FO TWS components. By refining the spatial and spectral alignment between observations and model results, this approach enhances the representation of individual water storage components, such as soil water and groundwater storage changes.
The proposed method involves several key steps to ensure data consistency within the multi-sensor fusion. First, all input datasets, including hydrological model outputs and remote sensing observations, are filtered to align their spectral signal contents. Then, a hierarchical Markov Chain Monte Carlo (MCMC) algorithm is applied to constrain all modeled and filtered TWS with GRACE/GRACE-FO and the SSM datasets. This is achieved by computing a temporal scaling factor that aligns the individual water storage compartments of the hydrological model with both observations. Finally, the residuals between filtered and unfiltered model outputs are incorporated to refine TWS estimates and enhance the downscaling process. The implementation and validation of the proposed approach are demonstrated using the W3RA hydrological model at a 10 km resolution over Europe. Model performance is evaluated by comparing updated groundwater and topsoil water estimates with other model outputs such as WGHM and independent observations. Results highlight the effectiveness of the hierarchical Bayesian method in resolving spectral and spatial mismatches. This study underscores the potential of advanced Bayesian techniques to enhance the utility of remote sensing data in hydrological applications.
How to cite: mehrnegar, N. and forootan, E.: How Can a Hierarchical Bayesian Approach Bridge the Gap Between Multi-Source Remote Sensing Data and Hydrological Models?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16974, https://doi.org/10.5194/egusphere-egu25-16974, 2025.
Hydrological research benefits from a growing number and diversity of hydrological datasets. At the same time, the consistency across the increasing suite of datasets is unclear, limiting the comparability of findings derived with different datasets and variables. Here, we find overall low consistency of numerous state-of-the-art precipitation, evapotranspiration, runoff, and soil moisture datasets in terms of the water balance. Consistency is inferred between variations in soil moisture and in precipitation minus evapotranspiration minus runoff, where datasets are combined with independent datasets representing the remaining water balance variables. Highest consistency in the case of precipitation datasets is generally found for satellite-based datasets, while gauge-based datasets performed better in Northern Hemisphere regions with dense in-situ observations. In the case of evapotranspiration, highest consistency is found for satellite-based and reanalysis datasets, and in the case of runoff for gauge-based and reanalysis datasets. Reanalysis soil moisture datasets that consider deep soil water dynamics show higher consistency than satellite-based or gauge-based datasets. Spatial variations of consistency are mostly related to aridity and temperature as they influence precipitation measurement quality. Soil moisture dataset consistency is additionally affected by vegetation cover. We find widespread increases in dataset consistency in the northern mid-latitudes during the study period, probably related to climate warming.
How to cite: Huang, H., Liu, J., Chen, A., and Orth, R.: Low water balance consistency of state-of-the-art hydrological datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11722, https://doi.org/10.5194/egusphere-egu25-11722, 2025.
Understanding long-term trends in Essential Climate Variables (ECVs) is important for predicting future climate impacts. This study investigates long-term trends in multiple land and climate variables, including evapotranspiration (ET), surface soil moisture (SM), snow cover (SC), snow water equivalent (SWE), total water storage (TWS) and streamflow along with variables influencing vegetation productivity, such as vapor pressure deficit (VPD), gross primary production (GPP) and plant available water, to provide a comprehensive assessment of their changes and interactions. Using a combination of observational datasets, remote sensing products, and reanalysis data, we evaluate the performance of Community Land Model, version 5.0 (CLM5) in capturing these trends over the European continent during the past 33 years (1990 - 2022). Additionally, we present a multi-model ensemble of CLM5 simulations with different configurations (Prescribed vs. prognostic vegetation) and different model resolution (0.0275o vs. 0.11o) to assess uncertainties in capturing trends arising from varying model complexities. All model configurations are driven by the ERA5 reanalysis dataset and share consistent datasets for the static input datasets such as topography, land cover and soil properties.
Our preliminary analysis shows that the CLM5 model captures the interannual variability in the hydrologic states and fluxes reasonably well for ET, SWE, SC and TWS, but overestimates surface SM to satellite-derived datasets. Model performance in capturing trends varies across variables: while decreasing trend direction in snowpack variables (SC and SWE) and TWS align with remote sensing observations, surface SM trends show opposite directions. We further explore whether these discrepancies arise from trends in climatic drivers (e.g., temperature and precipitation) or differences in model configurations.This study highlights the importance of a multivariate approach to trend analysis in improving our understanding of the recent states and changes in land surface variables.
How to cite: Naz, B. S., Springer, A., Terán, C. P., Ewerdwalbesloh, Y., Zhao, H., Jendges, L., Brockmann, J. M., Montzka, C., Guan, B., Sivaprasad, V., and Franssen, H.-J. H.: Assessing the performance of pan-European CLM5 simulations in capturing long-term multivariate trends in land surface varaibles., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15914, https://doi.org/10.5194/egusphere-egu25-15914, 2025.
LISFLOOD is a comprehensive large-scale operational hydrological model widely used in Europe to simulate diverse hydrological processes, including snowmelt, which is handled through a degree-day-based snow module (Van Der Knijff et al., 2010). The snowmelt coefficient in this module is traditionally calibrated against discharge data. This study evaluates the performance of LISFLOOD’s current snow module and explores an alternative calibration approach based on snow cover area (SCA) observations. Nine hydrological basins across Europe located in Italy, Switzerland, Austria, Germany, France, Spain, Slovakia, and Sweden were selected for this analysis. They represent a range of climatic and morphological characteristics, from mountainous regions such as the Alps and Pyrenees to the flatter terrains of Scandinavia. Their strong snow influence, with persistent snow cover for significant portions of the year, makes them ideal for assessing snow processes.
First, we evaluated several operational satellite-based snow cover products. This included an intercomparison of data gaps and agreements, benchmarked against a novel product that integrates Sentinel-2 and MODIS datasets using gap-filling and downscaling techniques to achieve high temporal and spatial resolution (Premier et al., 2021). Next, the snowmelt coefficient was estimated on a pixel-wise basis by fitting the modeled snow cover fraction (SCF) -derived from snow water equivalent (SWE) in LISFLOOD - with observed satellite-based SCF. This involved an appropriate parametrization to convert SWE to SCF and an optimization routine to minimize errors between modeled and observed SCF. The resulting spatially distributed snowmelt coefficient represents a novelty compared to the current LISFLOOD setup, where coefficients are uniform across subcatchments.
Our findings show that LISFLOOD’s current configuration performs well when validated against independent satellite-based snow cover products. While the newly optimized snowmelt coefficients differ considerably from previously calibrated values, they do not introduce significant changes in terms of simulated discharge. However, notable effects are observed in the timing and magnitude of SWE and snowmelt processes, underscoring the potential for improved representation of snow dynamics in LISFLOOD.
References
Premier, V., Marin, C., Steger, S., Notarnicola, C., & Bruzzone, L. (2021). A novel approach based on a hierarchical multiresolution analysis of optical time series to reconstruct the daily high-resolution snow cover area. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 9223-9240.
Van Der Knijff, J. M., Younis, J., & De Roo, A. P. J. (2010). LISFLOOD: a GIS‐based distributed model for river basin scale water balance and flood simulation. International Journal of Geographical Information Science, 24(2), 189-212.
How to cite: Premier, V., Moschini, F., Casado-Rodríguez, J., Bavera, D., Marin, C., and Pistocchi, A.: Evaluating the Snow Module of the LISFLOOD Model with Remotely Sensed Snow Cover , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11891, https://doi.org/10.5194/egusphere-egu25-11891, 2025.
Hydrological modeling is an essential tool for understanding and describing hydrological processes, serving as a cornerstone in the quantification and management of water resources. The major challenge of hydrological modeling lies in model calibration, which becomes particularly demanding in large-scale applications and in data-scarce regions.
Data scarcity is a significant constraint in modeling, complicating the calibration process and reducing model accuracy. Generally, the availability of high-quality streamflow measurements is considered vital for the calibration and evaluation of hydrological models. However, in many scenarios data may be of low quality, incomplete, or entirely unavailable, as it happens in many areas of the National Territory, including regions in Southern Italy where the streamflow observations are limited, fragmented and discontinuous. Most hydrometric stations record only water levels, often without updated flow rating curves, making reliable hydrological model calibration a challenging task.
In order to overcome such limitations, we compared three different setups to get the best parametrization during the model calibration. At first, we used the biggest hydrological basin (Volturno river catchment) of the entire district, as representative of the regional study area. The calibration of the model was done for the representative catchment, and the parameters were applied at the regional scale. Then, we used reconstructed streamflow measurements derived from water balance of nine artificial reservoirs as a reference for a multiobjective calibration. At last, we used remote sensing data, such as soil moisture maps, as a reference for calibrating the model. Multi-objective functions, focusing on high-flows and low-flows aspects of the time series, were used in automatic optimization based on genetic algorithms to perform space-time operational testing of the large-scale model. The reference hydrological model used is the DREAM model (Distributed model for Runoff, Evapotranspiration, and Antecedent Soil Moisture simulation), applied to the vast area within the jurisdiction of the Southern Apennine District Basin Authority.
These calibration procedures have been compared exploiting available data. The study provides guidance in the use of limited data in order to identify the most suitable approach to build a reliable model calibration of the entire district and assess the impact of climate change on water resources in future climate scenarios. The encouraging performances of the regional model motivate the extension of the present approach to other data-scarce regions.
How to cite: Pacia, F. D., Perrini, P., Avino, A., Sarwar, A. N., Jahanshahi, A., Coccaro, P., Giuzio, L., Corbelli, V., Fiorentino, M., Iacobellis, V., and Manfreda, S.: Water Availability Assessment in a Data-Scarce Region: Application to the District of Southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9153, https://doi.org/10.5194/egusphere-egu25-9153, 2025.
The Tigris-Euphrates River Basin (TERB) is a transboundary water system of critical importance in West Asia, spanning Turkey, Syria, Iraq, and Iran. Its water resources support agriculture, industries, and hydropower, and water flows and storages are significantly affected by human interventions, including dam construction, water abstractions, and artificial water transfers. While a few global hydrological models, such as the WaterGAP Global Hydrology Model (WGHM), include human interventions like reservoirs, as well as surface water and groundwater use, including water transfers between adjacent grid cells, long-distance artificial water transfers, are not simulated except by the global hydrological model H08 (Hanasaki et al. 2018). H08 includes the location of 55 aqueducts but all outside of the TERB. However, they assume, for lack of information on transferred water flows, that water flows correspond to the demand for surface water abstractions in grid cells connected to the aqueduct until the river flow at the origin of the aqueduct becomes zero. This assumption certainly does not represent most water transfers well. In this study, for the first time, we modeled long-distance artificial water transfer in WGHM as the Tigris-to-Euphrates water transfer via Lake Tharthar is crucial for the water flows and storages in the TERB. Based on an analysis of observed streamflow data for 2002-2021 at the Baghdad station (downstream of the lake on the Tigris) and the volume of water transferred to the lake from the Tigris at the Samarra site, we developed a diversion algorithm. The algorithm directs streamflow above a given threshold (534 m3/sec) from December to July to Lake Tharthar to maintain stable streamflow at the Baghdad station, consistent with observations. Lake Tharthar is treated as a regulated lake instead of an inland sink, with its outflow transferred to the Euphrates River. Results demonstrate that simulating water transfers between the Tigris and Euphrates Rivers improves the accuracy of streamflow simulations at the Baghdad station. The mean simulated streamflow at the Baghdad station for the standard WGHM simulation was 1052 m³/sec, which, after modification, was reduced to 561 m³/sec, bringing it much closer to the mean observed value of 525 m³/sec. Additionally, the variability of the simulated streamflow in relation to the observed values (the ratio of the standard deviation of the simulated streamflow to the standard deviation of the observed streamflow (117 m³/sec)) improved from 5.4 for the standard WGHM to 2.4 for the modified simulation. These findings highlight the necessity of integrating artificial water transfers into hydrological models to better capture the alteration of natural water flows and storages.
References
Hanasaki, N., Yoshikawa, S., Pokhrel, Y., & Kanae, S. (2018). A global hydrological simulation to specify the sources of water used by humans. Hydrology and Earth System Sciences, 22(2), 789–817. https://doi.org/10.5194/hess-22-789-2018
How to cite: Hasan, A., Hosseini-Moghari, S.-M., and Döll, P.: Incorporating Artificial Water Transfers from the Tigris to the Euphrates into the WaterGAP Global Hydrology Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9965, https://doi.org/10.5194/egusphere-egu25-9965, 2025.
